In agricultural farms in Indiawhere the staple diet formost of the households is potato,plant leaf diseases,namely Potato Early Blight(PEB)and Potato Late Blight(PLB),are quite common.The class label Plant Healthy(PH)...In agricultural farms in Indiawhere the staple diet formost of the households is potato,plant leaf diseases,namely Potato Early Blight(PEB)and Potato Late Blight(PLB),are quite common.The class label Plant Healthy(PH)is also used.If these diseases are not identified early,they can causemassive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation.This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy leaves,combining the strengths of classical image processing,computer vision,and deep learning.We propose a pipeline that initially employs OpenCV’s cv2 led color-based image segmentation to isolate and highlight diseased brown,yellowcolored lesions or regions and healthy green colored lesion areas associated with various potato leaf diseases.Adaptive Thresholding for illumination and texture feature extraction and U-Net Segmentation for mask refinement for severity estimation.It has a mathematical framework for quantifying the severity based on the spatial area distribution of these regions.This allows for both visual representation of the segmented regions in the form of overlay masks and quantification of distinct disease severity.We detail the implementation of the approach,including color space selection,segmentation strategies,mask creation,area calculation,and a potential mathematical model for severity calculation.Overlay masks generated are then used as input to a CBAM-EfficientNetB0 model,leveraging transfer learning for improved classification accuracy and efficiency.For the Plant Village dataset,the test accuracy achieved is 0.99,whereas the test loss is 0.02,respectively.For the Plant Doc dataset,the test accuracy achieved is 0.97,whereas the test loss is 0.06,respectively.Also,the CBAM attention mechanism model lays emphasis on relevant features within the lesions and overall image context.The results achieved with the Plant Village dataset are slightly better in comparison to the Plant Doc dataset.展开更多
针对线段因遮挡、断裂以及端点提取不准确等原因造成的线段特征匹配困难问题,特别是现有匹配算法在匹配过程中出现"多配多"时直接采取"最相似匹配"而导致丢失大量真实匹配的问题,提出了一种基于多重几何约束及0-1...针对线段因遮挡、断裂以及端点提取不准确等原因造成的线段特征匹配困难问题,特别是现有匹配算法在匹配过程中出现"多配多"时直接采取"最相似匹配"而导致丢失大量真实匹配的问题,提出了一种基于多重几何约束及0-1规划的线段特征匹配算法。首先,基于校正后视频帧间线段特征的空间相邻性计算线段匹配的初始候选集;然后,基于极线约束、单应矩阵模型约束以及点-线相邻性约束等多重几何约束,对候选集进行筛选从而剔除部分错误匹配;其次,将线段匹配问题建模为一个大规模0-1规划问题;最后,设计了一种基于分组策略的两阶段求解算法对该问题进行求解,从而实现线段特征的"一配一"精确匹配。实验结果表明,该算法与LS(Line Sigature)、LJL(LineJunction-Line)方法相比,匹配正确率接近,但匹配线段数量分别提高了60%和11%。所提算法可以实现视频帧间的线段特征匹配,为基于线特征的视觉SLAM(Simultaneously Localization and Mapping)奠定基础。展开更多
基金done under Department of Biotechnology(DBT)project titled“Application of Machine Learning for Hyperspectral Imaging and Remote Sensing aimed at Early Detection of Fungal Foliar Diseases and Bacterial Wilt Diseases in Potato Crop”,DBT/Reference.No.BT/PR45388/133/58/2022.
文摘In agricultural farms in Indiawhere the staple diet formost of the households is potato,plant leaf diseases,namely Potato Early Blight(PEB)and Potato Late Blight(PLB),are quite common.The class label Plant Healthy(PH)is also used.If these diseases are not identified early,they can causemassive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation.This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy leaves,combining the strengths of classical image processing,computer vision,and deep learning.We propose a pipeline that initially employs OpenCV’s cv2 led color-based image segmentation to isolate and highlight diseased brown,yellowcolored lesions or regions and healthy green colored lesion areas associated with various potato leaf diseases.Adaptive Thresholding for illumination and texture feature extraction and U-Net Segmentation for mask refinement for severity estimation.It has a mathematical framework for quantifying the severity based on the spatial area distribution of these regions.This allows for both visual representation of the segmented regions in the form of overlay masks and quantification of distinct disease severity.We detail the implementation of the approach,including color space selection,segmentation strategies,mask creation,area calculation,and a potential mathematical model for severity calculation.Overlay masks generated are then used as input to a CBAM-EfficientNetB0 model,leveraging transfer learning for improved classification accuracy and efficiency.For the Plant Village dataset,the test accuracy achieved is 0.99,whereas the test loss is 0.02,respectively.For the Plant Doc dataset,the test accuracy achieved is 0.97,whereas the test loss is 0.06,respectively.Also,the CBAM attention mechanism model lays emphasis on relevant features within the lesions and overall image context.The results achieved with the Plant Village dataset are slightly better in comparison to the Plant Doc dataset.
文摘针对线段因遮挡、断裂以及端点提取不准确等原因造成的线段特征匹配困难问题,特别是现有匹配算法在匹配过程中出现"多配多"时直接采取"最相似匹配"而导致丢失大量真实匹配的问题,提出了一种基于多重几何约束及0-1规划的线段特征匹配算法。首先,基于校正后视频帧间线段特征的空间相邻性计算线段匹配的初始候选集;然后,基于极线约束、单应矩阵模型约束以及点-线相邻性约束等多重几何约束,对候选集进行筛选从而剔除部分错误匹配;其次,将线段匹配问题建模为一个大规模0-1规划问题;最后,设计了一种基于分组策略的两阶段求解算法对该问题进行求解,从而实现线段特征的"一配一"精确匹配。实验结果表明,该算法与LS(Line Sigature)、LJL(LineJunction-Line)方法相比,匹配正确率接近,但匹配线段数量分别提高了60%和11%。所提算法可以实现视频帧间的线段特征匹配,为基于线特征的视觉SLAM(Simultaneously Localization and Mapping)奠定基础。
